def main(): import sys import Orange argv = sys.argv if len(argv) > 1: filename = argv[1] else: filename = 'iris' app = QtGui.QApplication(argv) ow = OWPythagorasTree() data = Orange.data.Table(filename) if data.domain.has_discrete_class: from Orange.classification.tree import TreeLearner else: from Orange.regression.tree import TreeLearner model = TreeLearner(max_depth=1000)(data) model.instances = data ow.set_tree(model) ow.show() ow.raise_() ow.handleNewSignals() app.exec_() sys.exit(0)
def main(argv=sys.argv): from AnyQt.QtWidgets import QApplication import Orange app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = 'iris' ow = OWPythagorasTree() data = Orange.data.Table(filename) if data.domain.has_discrete_class: from Orange.classification.tree import TreeLearner else: from Orange.regression.tree import TreeLearner model = TreeLearner(max_depth=1000)(data) model.instances = data ow.set_tree(model) ow.show() ow.raise_() ow.handleNewSignals() app.exec_() sys.exit(0)
def main(): import sys import Orange argv = sys.argv if len(argv) > 1: filename = argv[1] else: filename = 'iris' app = QtGui.QApplication(argv) ow = OWPythagorasTree() data = Orange.data.Table(filename) if data.domain.has_discrete_class: from Orange.classification.tree import TreeLearner model = TreeLearner(max_depth=1000)(data) else: from Orange.regression.tree import TreeRegressionLearner model = TreeRegressionLearner(max_depth=1000)(data) model.instances = data ow.set_tree(model) ow.show() ow.raise_() ow.handleNewSignals() app.exec_() sys.exit(0)
def test_report_widgets_classify(self): rep = OWReport.get_instance() data = Table("titanic") widgets = self.clas_widgets w = self.create_widget(OWTreeGraph) clf = TreeLearner(max_depth=3)(data) clf.instances = data w.ctree(clf) w.create_report_html() rep.make_report(w) self._create_report(widgets, rep, data)
def test_report_widgets_model(self): rep = OWReport.get_instance() data = Table("titanic") widgets = self.model_widgets w = self.create_widget(OWTreeGraph) clf = TreeLearner(max_depth=3)(data) clf.instances = data w.ctree(clf) w.create_report_html() rep.make_report(w) self._create_report(widgets, rep, data)
def test_report_widgets_classify(self): rep = OWReport.get_instance() data = Table("zoo") widgets = self.clas_widgets w = OWClassificationTreeGraph() clf = TreeLearner(max_depth=3)(data) clf.instances = data w.ctree(clf) w.create_report_html() rep.make_report(w) self.assertEqual(len(widgets) + 1, 8) self._create_report(widgets, rep, data)
def toggle_node_color(self): colors = self.scene.colors for node in self.scene.nodes(): distr = node.get_distribution() total = numpy.sum(distr) if self.target_class_index: p = distr[self.target_class_index - 1] / total color = colors[self.target_class_index - 1].light(200 - 100 * p) else: modus = node.majority() p = distr[modus] / (total or 1) color = colors[int(modus)].light(400 - 300 * p) node.backgroundBrush = QBrush(color) self.scene.update() if __name__ == "__main__": from Orange.classification.tree import TreeLearner a = QApplication(sys.argv) ow = OWClassificationTreeGraph() data = Table("iris") clf = TreeLearner(max_depth=3)(data) clf.instances = data ow.ctree(clf) ow.show() ow.raise_() a.exec_() ow.saveSettings()